The invention relates generally to an interconnect system, and, more particularly, to a system in which a planar input array is optically interconnected to a planar output array using a holographic planar array to effect such interconnections.
In the field of computing, the need for ever faster and faster computers is a continuing requirement. Conventional computers have a single processor acting on meticulously programmed instructions. Tasks are broken down into small segments and performed in sequence very rapidly. This process places severe limitations on the application of such computers to many tasks including those involving vision, speech recognition and complex multi-variable problems. Efforts to remove such limitations through increased speed of operation of single processor computers have met with some success but are now approaching the limitations of electronics technology due to the interaction between electron carrying conductors ever present in electronic systems.
In recent years, computer scientists and engineers have developed system architectures which provide for multi-processors in a computer system. In such a system, the problem to be processed on the computer is divided into logical segments with each segment being assigned to one of the multi-processors in the system and processed in parallel all under the control of a system control processor which performs over all system control, problem segmentation and processor assignments, collecting the results of processing for each segment and performing the processing needed to produce the final solution to the problem. Systems utilizing such architectures are now beginning to be used in limited applications. However, a major problem with such systems is the need to design and program the operating system software required for the efficient operation of such systems. This need is only now beginning to be addressed and it is expected that it will be very costly to produce the required results.
Observations that humans perform task involving vision, speech recognition and complex multi-variable problems with ease have resulted in research into understanding the structure and operation of the human brain. Currently, it is generally believed that the human brain has billions of neurons, each of which is loosely or tightly connected to thousands of other neurons, all functioning in parallel and in concert. It is believed that the power of the brain results from the sheer number of neurons, their multiple interconnections and the parallel processing capabilities.
Scientists attempted to build brain like computers in the 1950's and 1960's, but due to severe technology limitations at that time, most efforts were abandoned and attention focused on conventional artificial intelligence or expert systems. This involved extensive rule programming of computers performing tasks such as reading, diagnostic and other human-like activities. Expert systems are based on heuristic rules that mimic the collective thinking of human experts and are usually implemented on electronic computers. Expert systems have had many successes, but to date they have had considerable problems with the tasks involving vision, speech recognition and complex variable problems. This has led to renewed interests in brain-like computers. In recent years, a great deal of research has been and is today being conducted in pursuit of such brain-like computers, often referred to as "neural network systems".
To date, the recent work in neural network systems has been directed to simulations of such networks by programming digital computers and investigations of the application of either electronics or optics technologies to the design of such systems. Unfortunately, the programming of digital computers to simulate neural networks is, for all but very simple tasks, expensive and, to date, limited effectiveness. The application of electronics technology to produce such neural networks is expected to advance the field over that which can be expected from the simulation approach, but inherently has the limitation of interaction between electron carrying conductors. Optics technology, due to the natuural characteristics that independent light particles do not interact with one another, does not have such limitations.
Recent research on neural networks has shown that even with a small number (hundreds) of neurons, these systems can be taught to perform very rudimentary tasks well beyond those that can be performed in current electronic digital computers. To apply neural network systems to problems of the complexity present in government, industry, business organizations, the number of neurons in the system must be increased substantially resulting in a requirement for massive interconnections between such neurons. The inherent limitations of electronics currently appears to restrict its use in providing such massive interconnections, but the holographic optical system of this invention provides an innovative solution.